Automatic Identification of Motor Patterns Leading to Freezing of Gait in Parkinson’s Disease - An Exploratory Study

Luca Palmerini, Laura Rocchi, Jeffrey M. Hausdorff, Lorenzo Chiari

2014

Abstract

Freezing of gait (FOG) is a common and disabling gait disturbance among patients with advanced Parkinson’s Disease (PD). FOG episodes are often overcome using attention or cues from the environment. Hence, identification of events prior to FOG may be very effective to improve mobility in PD patients. Previous work has suggested that there are changes in the gait pattern just prior to freezing. Nonetheless, little work has been done to explore the possibility of identifying motor patterns that are characteristic of the pre-FOG phase (few seconds before the FOG). We analysed the acceleration signals from sensors worn on the ankle, thigh, and trunk of eight patients with PD who experienced freezing. We translated windows of the raw signals in symbols by using Symbolic Aggregate approXimation. The aim was to discriminate the patterns of symbols characterizing pre-FOG from the ones characterizing normal activity (standing and walking with no FOG). Sensitivity over 50% and Specificity over 70% were obtained by using a classifier on symbolic data, with different combinations of sensor position/sampling/windows duration. These preliminary findings demonstrate that it is possible to automatically identify (some of) the motor patterns that eventually lead to FOG events before they occur by using wearable sensors.

References

  1. Bächlin M., Plotnik M., Roggen D., Maidan I., Hausdorff J. M., Giladi N., Tröster G., Wearable Assistant for Parkinson's Disease Patients With the Freezing of Gait Symptom, IEEE Trans on Information Technology in Biomedicine, 14(2), March 2010, pages 436-446.
  2. Lin J., Keogh E., Patel P., Lonardi S., Finding Motifs in Time Series, proceedings of the 2nd Workshop on Temporal Data Mining, 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada. July 23-26, 2002
  3. Lin J., Keogh E., Lonardi S., Chiu B., A Symbolic Representation of Time Series, with Implications for Streaming Algorithms, proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. June 13, 2003.
  4. Mazilu S., Hardegger M., Zhu Z., Roggen D., Tröster G., Plotnik M. and Hausdorff J. M., Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques, 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012
  5. Mazilu S., Calatroni A., Gazit E., Roggen D., Hausdorff J. M., and Tröster G., Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson's Disease, MLDM, Lecture Notes in Computer Science (LNCS), Springer 2013.
  6. Moore S. T., Yungher D. A., Morris T. R., Dilda V., MacDougall H. G., Shine J. M., Naismith S. L., Lewis S. J., Autonomous identification of freezing of gait in Parkinson's disease from lower-body segmental accelerometry. Journal of neuroengineering and rehabilitation, 10(1), 19. 2013
  7. Nieuwboer A., Dom R., De Weerdt W., Desloovere K., Janssens L., Stijn V., Electromyographic profiles of gait prior to onset of freezing episodes in patients with Parkinson's disease. Brain. 2004 Jul;127(Pt 7):1650- 60.
  8. Sant'Anna et al., “A new measure of movement symmetry in early Parkinson's disease patients using symbolic processing of inertial sensor data.,” IEEE trans on biomedical engineering, vol. 58, no. 7, pp. 2127-35, Jul. 2011.
Download


Paper Citation


in Harvard Style

Palmerini L., Rocchi L., M. Hausdorff J. and Chiari L. (2014). Automatic Identification of Motor Patterns Leading to Freezing of Gait in Parkinson’s Disease - An Exploratory Study . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 730-734. DOI: 10.5220/0004912107300734


in Bibtex Style

@conference{icpram14,
author={Luca Palmerini and Laura Rocchi and Jeffrey M. Hausdorff and Lorenzo Chiari},
title={Automatic Identification of Motor Patterns Leading to Freezing of Gait in Parkinson’s Disease - An Exploratory Study},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={730-734},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004912107300734},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Automatic Identification of Motor Patterns Leading to Freezing of Gait in Parkinson’s Disease - An Exploratory Study
SN - 978-989-758-018-5
AU - Palmerini L.
AU - Rocchi L.
AU - M. Hausdorff J.
AU - Chiari L.
PY - 2014
SP - 730
EP - 734
DO - 10.5220/0004912107300734